用深度学习提升风电场储能调度效率，降低弃风率。
Deep Learning for Modeling and Dispatching Hybrid Wind Farm Power Generation
- 基于LSTM的调度模型针对单个风电场优化
- 43年模拟中年弃风量降低32.3%
- 适合新能源调度与储能系统研究者
集成储能的混合风电场可通过运行策略存储并调度电能。针对具备储能能力的单个风电场，利用本地电网需求和市场条件作为输入参数的数据驱动调度策略，有助于最大化风能价值。基于大气条件生成的合成发电数据可提升数据驱动调度策略的鲁棒性。本文提出两种深度学习框架：COVE-NN是一种基于LSTM的定制化调度策略，在Pyron站点43年模拟中使年均弃风量（COVE）降低32.3%；另一发电建模框架在Palouse风电场验证中，将均方根误差（RMSE）降低9.5%，功率曲线相似度提升18.9%。两者共同推动更稳健的数据驱动调度，并可扩展至其他可再生能源系统。
原文摘要 · Abstract (English)
Wind farms with integrated energy storage, or hybrid wind farms, are able to store energy and dispatch it to the grid following an operational strategy. For individual wind farms with integrated energy storage capacity, data-driven dispatch strategies using localized grid demand and market conditions as input parameters stand to maximize wind energy value. Synthetic power generation data modeled on atmospheric conditions provide another avenue for improving the robustness of data-driven dispatch strategies. To these ends, the present work develops two deep learning frameworks: COVE-NN, an LSTM-based dispatch strategy tailored to individual wind farms, which reduced annual COVE by 32.3% over 43 years of simulated operations in a case study at the Pyron site; and a power generation modeling framework that reduced RMSE by 9.5% and improved power curve similarity by 18.9% when validated on the Palouse wind farm. Together, these models pave the way for more robust, data-driven dispatch strategies and potential extensions to other renewable energy systems.
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